1. Models developed by three techniques did not achieve acceptable prediction of binary trauma outcomes
- Author
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Wolfe, Rory, McKenzie, Dean P., Black, James, Simpson, Pam, Gabbe, Belinda J., and Cameron, Peter A.
- Subjects
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TRAUMA centers , *BIOLOGICAL neural networks - Abstract
Abstract: Background and Objectives: To develop prediction models for outcomes following trauma that met prespecified performance criteria. To compare three methods of developing prediction models: logistic regression, classification trees, and artificial neural networks. Methods: Models were developed using a 1996–2001 dataset from a major trauma center in Victoria, Australia. Developed models were subjected to external validation using the first year of data collection, 2001–2002, from a state-wide trauma registry for Victoria. Different authors developed models for each method. All authors were blinded to the validation dataset when developing models. Results: Prediction models were developed for an intensive care unit stay following trauma (prevalence 23%) using information collected at the scene of the injury. None of the three methods gave a model that satisfied the performance criteria of sensitivity >80%, positive predictive value >50% in the validation dataset. Prediction models were also developed for death (prevalence 2.9%) using hospital-collected information. The performance criteria of sensitivity >95%, specificity >20% in the validation dataset were not satisfied by any model. Conclusion: No statistical method of model development was optimal. Prespecified performance criteria provide useful guides to interpreting the performance of developed models. [Copyright &y& Elsevier]
- Published
- 2006
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